Radio transmitters amplify input signals. It is desired that the gain of such transmitters be linear for the entire range of input signals. Memoryless linearization of signal transmitters and, in particular, of radio transmitters is closely related to the problem of power amplifier linearization using baseband techniques, which is considered to be of the greatest significance for achieving effective and economical minimization of transmission-related signal distortions in digital communication systems.
Despite a big diversity of existing approaches aimed at improving the quality of RF power amplification, many of the older solutions are constrained to usage with specific discrete-level signaling formats, and, thus have a limited relevance to contemporary wideband communication standards. Development of general solutions of a particular practical value that are invariant with respect to the transmitted signal has been simulated in the past decade, pointing out the usefulness of a single-argument complex gain function of the input power for the modeling of memoryless distortions in baseband power amplifier linearizers.
Compared to the previously demonstrated general approach using two-dimensional mapping of the amplifier output against the phase and magnitude signal values at its input, the gain-based nonlinear model has a substantially lower computational complexity for the same performance that is instrumental in the design of hardware-efficient digital linearization systems.
A common architecture of recently proposed baseband power amplifier linearizers includes a digital nonlinear gain block, usually called a predistortion block, inserted in the transmitter chain prior to upconversion stages. The predistortion block may be continuously adapted to approximate as closely as possible the inverse nonlinear complex gain of the following transmitter stages up to the power amplifier. Depending on the coordinate system in which the transmitter gain estimation is conducted, two main types of baseband linearization approaches can be distinguished: (1) orthogonal-coordinate, where the complex gain function is defined by a pair of real and imaginary functions, and (2) polar-coordinate, where the complex gain function is defined by a magnitude and a phase function. Since in Quadrature Amplitude Modulation (QAM) schemes the signals are typically represented by in-phase and quadrature-phase components, i.e. in orthogonal coordinates, the realization of the second approach involves additional complexity to provide for coordinate system transformation of the estimation data. On the other hand, more sophisticated predistorter architectures and adaptation algorithms may be required for the implementation of unconditionally convergent and robust baseband linearization in orthogonal coordinates to account for high-power memory effects as a function of the input signal bandwidth and dynamic-range.